Statistical learning and recognition methods were used to extract the characteristics of size series measurements of cocoon filaments that are non-stationary in terms of mean and auto-covariance, by using the time var...Statistical learning and recognition methods were used to extract the characteristics of size series measurements of cocoon filaments that are non-stationary in terms of mean and auto-covariance, by using the time varying parameter auto-regressive （TVPAR） model. After the system was taught to recognize the size data, the system correctly recognized the size of series of cocoon filaments as much as 96.95% of the time for a single series and 98.72% of the time for the mean of two series. The correct recognition rate was higher after suitable filtering. The theory and method can be used to analyze other types of non-stationary finite length time series.展开更多
The chaotic characteristics of time series of five partial discharge （PD） patterns in oil-paper insulation are studied. The results verify obvious chaotic characteristic of the time series of discharge signals and t...The chaotic characteristics of time series of five partial discharge （PD） patterns in oil-paper insulation are studied. The results verify obvious chaotic characteristic of the time series of discharge signals and the fact that PD is a chaotic process. These time series have distinctive features, and the chaotic attractors obtained from time series differed greatly from each other by shapes in the phase space, so they could be used to qualitatively identify the PD patterns. The phase space parameters are selected, then the chaotic characteristic quantities can be extracted. These quantities could quantificationally characterize the PD patterns. The effects on pattern recognition of PRPD and CAPD are compared by using the neural network of radial basis function. The results show that both of the two recognition methods work well and have their respective advantages. Then, both the statistical operators under PRPD mode and the chaotic characteristic quantities under CAPD mode are selected comprehensively as the input vectors of neural network, and the PD pattern recognition accuracy is thereby greatly improved.展开更多
基金Supported by the Natural Science Foundation of Jiangsu Province, China (No. L0313419913)
文摘Statistical learning and recognition methods were used to extract the characteristics of size series measurements of cocoon filaments that are non-stationary in terms of mean and auto-covariance, by using the time varying parameter auto-regressive （TVPAR） model. After the system was taught to recognize the size data, the system correctly recognized the size of series of cocoon filaments as much as 96.95% of the time for a single series and 98.72% of the time for the mean of two series. The correct recognition rate was higher after suitable filtering. The theory and method can be used to analyze other types of non-stationary finite length time series.
基金supported by National Natural Science Foundation of China(No.50877064)
文摘The chaotic characteristics of time series of five partial discharge （PD） patterns in oil-paper insulation are studied. The results verify obvious chaotic characteristic of the time series of discharge signals and the fact that PD is a chaotic process. These time series have distinctive features, and the chaotic attractors obtained from time series differed greatly from each other by shapes in the phase space, so they could be used to qualitatively identify the PD patterns. The phase space parameters are selected, then the chaotic characteristic quantities can be extracted. These quantities could quantificationally characterize the PD patterns. The effects on pattern recognition of PRPD and CAPD are compared by using the neural network of radial basis function. The results show that both of the two recognition methods work well and have their respective advantages. Then, both the statistical operators under PRPD mode and the chaotic characteristic quantities under CAPD mode are selected comprehensively as the input vectors of neural network, and the PD pattern recognition accuracy is thereby greatly improved.
